this project implements action recognition algorithm proposed in C3D: Generic Features for Video Analysis with esimator of Tensorflow

c3d is a convolutional neural network classifying sports video clips. it is widely used as an infrastructure of latter action recognition neural networks.

a trained model is provided on baidu cloud at

https://pan.baidu.com/s/1pD4R0k23HOi_RIrLGZSlOg

if you want to train yourself, you need UCF101 dataset. download it and extract the directory. set the root directory in create_dataset.py. then create a tfrecord format dataset with command

python3 create_dataset.py

the program will generate a trainset and a testset tfrecord file.

start the training by exeucting

python3 train_c3d.py

moniter the training process by tensorboard and stop the training when the accuracy reaches 82% which is the best accuracy c3d can reach.

test on single video clip by modifying the video path in ActionRecognition.py and run with

python3 ActionRecognition.py

a readable label will be printed on the video.

run following command to get serving model

python3 convert_model.py